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27th International Conference on Pattern Recognition, ICPR 2024

作者： Hasan, Kazi Reyazul Adnan, Muhammad Abdullah Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9783031783043

In this paper, we introduce EMPATH, an advanced computational framework developed to substantially enhance the recognition of Bangla Sign Language (BdSL). By integrating Ensemble Learning, MediaPipe Holistic for gesture tracking, and an Attention-based Transformer model, EMPATH addresses the challenges of sign language interpretation, setting new accuracy benchmarks and significantly surpassing previous records: achieving 79.81% on SignBD-Word-90 (previously best at 66.05%), 70.58% on SignBD-Word (previously best at 57%), and 99.25% on BdSL40 (previously best at 89%). A pioneering feature of EMPATH is its innovative interpolation model, built to overcome the limitations posed by missing Hand keypoints of MediaPipe. Validated in both EMPATH and a basic MLP framework, this model showcases remarkable versatility across architectures. EMPATH strategically selects its preprocessing and postprocessing techniques, optimizing each for maximum impact on accuracy and performance. Extensively trained across various word-level datasets beyond Bangla, including INCLUDE-50, INCLUDE, WLASL-100, and the Malaysian Sign Language Medical Dataset, EMPATH demonstrates its broad applicability and global potential. This diverse training scheme establishes superior accuracy benchmarks: achieving an impressive 100% on INCLUDE-50, 94.67% on INCLUDE, and 93.46% on the MSL Medical Dataset. Through EMPATH, we aspire to bridge communication barriers for the deaf and hard-of-hearing communities, showcasing the profound impact of integrating advanced technological solutions to tackle the complexities of sign language recognition. Source code is available at https://***/kreyazulh/EMPATH. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Benchmarking

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Thermal Homography in Photovoltaic Panels: Evaluating Deep Learning and Feature-Based Methods

Thermal Homography in Photovoltaic Panels: Evaluating Deep L...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Dahmani, Hajer Yaqoob, Mohammed Ansari, Mohammed Yusuf Flushing, Eduardo Feo Carnegie Mellon University in Qatar Computer Science Doha Qatar Texas A&M University Electrical and Computer Engineering College StationTX United States Texas A&M University at Qatar Electrical and Computer Engineering Doha Qatar

ISBN: (纸本)9798331541125

Thermal imaging has become a vital tool for analyzing temperature variations in various fields, including medical diagnostics, industrial inspection, and environmental monitoring. However, the application of homography techniques in the thermal imaging domain, particularly for photovoltaic (PV) panels, remains underexplored. This paper presents a comprehensive evaluation of state-of-the-art deep homography models, specifically HomographyNet, in comparison with traditional feature-based methods such as ORB+RANSAC and SIFT+RANSAC. These methods are applied to close-range thermal images of PV panels, and their performance is assessed using the mean average corner error (MACE) metric. Extensive experiments analyze the accuracy and robustness of these techniques, as well as the influence of colormap representations on model performance. Key contributions include the evaluation of homography methods for thermal imaging, an in-depth analysis of colormap effects, and the introduction of a novel high-resolution thermal image dataset for PV panels. Results demonstrate that HomographyNet outperforms traditional methods, achieving a MACE of 13.65 with the Noon dataset compared to 24.78 with the Morning dataset. HomographyNets superior performance with magma colormap makes it particularly useful in PV applications such as identifying microcracks or hotspots in solar cells, where accurate thermal image alignment can enhance the visualization of subtle yet critical temperature anomalies. © 2025 IEEE.

关键词： Thermography (imaging)

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Cricket Team Selection and Player Analysis using Data Analytics 6

Cricket Team Selection and Player Analysis using Data Analyt...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Raajesh, Sanjay Martin, Noel Jiji, Jyothsna Nair, Aakash Haritha, H. Muthoot Institute of Technology and Science Department of Computer Science and Engineering India

ISBN: (纸本)9798350381689

The inception of the 'Cricket Team Selection and Player Analysis using Data Analytics' project arose from the imperative to introduce precision through data-driven methods in the process of selecting cricket teams. Recognizing the pivotal role of team composition in the sport, our project employs the K-means clustering algorithm to categorize players based on their historical performance data with the strike rate. This approach facilitates the streamlined creation of well-balanced teams, targeting cricket enthusiasts, coaches, and team selectors. The ultimate goal is to enhance their decision-making processes, optimizing team performance and strategy. The project adopts a comprehensive approach, heralding a new era in cricket by leveraging data. It enables the formation of playing elevens derived from historical player performance metrics, ensuring a more resilient and strategic selection process. The project also has a selection of top players based on the attributes provided by the user. The tangible outcome of this project is a tool designed to serve cricket stakeholders, aiding them in making informed decisions for improved performance on the field. Representing a significant stride towards a more data-driven approach to cricket, the 'Cricket Team Selection and Player Analysis using Data Analytics' project is poised to redefine team selection and strategy in the sport. Its applicability extends to a broad audience, promising to be a transformative force in the realm of cricket decision-making. © 2024 IEEE.

关键词： Support vector machines

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A Privacy-preserving Energy Management System Based on Homomorphic Cryptosystem for IoT-enabled Active Distribution Network

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 167-178页

作者： Qian Hu Siqi Bu Wencong Su Vladimir Terzija the Department of Physics Hong Kong Baptist UniversityHong KongChina the Department of Electrical and Electronic Engineering Shenzhen Research InstituteThe Hong Kong Polytechnic UniversityHong KongChina the Department of Electrical and Computer Engineering University of Michigan-DearbornDearbornMI 48128USA the School of Engineering Newcastle UniversityNewcastleUK

Active distribution network(ADN),as a typically cyber-physical system,develops with the evolution of Internet of Things(IoTs),which makes the network vulnerable to cybersecurity *** this paper,the eavesdropping attacks that lead to privacy breaches are addressed for the IoT-enabled ADN.A privacy-preserving energy management system(EMS)is proposed and empowered by secure data exchange protocols based on the homomorphic *** the information transmission among distributed generators and load customers in the EMS,private information including power usage and electricity bidding price can be effectively protected against eavesdropping *** correctness of the final solutions,e.g.,optimal market clearing price and unified power utilization ratio,can be deterministically *** simulation results demonstrate the effectiveness and the computational efficiency of the proposed homomorphically encrypted EMS.

关键词： Eavesdropping attack energy management system homomorphic cryptosystem Internet of Things(IOTs) active distribution network(ADN) privacy-preserving

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A top-down character segmentation approach for Assamese and Telugu handwritten documents

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3275-3287页

作者： Dutta, Prarthana Muppalaneni, Naresh Babu Department of Computer Science and Engineering National Institute of Technology Assam Silchar786003 India Department of Computer Science and Engineering The Assam Kaziranga University Assam Jorhat785006 India Indian Institute of Information Technology Design and Manufacturing Kurnool Andhra Pradesh Kurnool India

Digitization offers a solution to the challenges associated with managing and retrieving paper-based documents. However, these paper-based documents must be converted into a format that digital machines can comprehend, as they primarily understand alphanumeric text. This transformation is achieved through Optical Character Recognition (OCR), a technology that converts scanned image documents into a format that machines can process. A novel top-down character segmentation approach has been proposed in this work, involving multiple stages. Our approach began by isolating lines from handwritten documents and using these lines to segment words and characters. To further enhance the character segmentation, a Raster Scanning object detection technique is employed to isolate individual characters within words. Thus, the character segmentation results are integrated from the results of the vertical projection and raster scanning. Recognizing the significance of advancing digitization of handwritten documents, we have chosen to focus on the regional languages of Assam and Andhra Pradesh due to their historical and cultural importance in India’s linguistic diversity. So, we have collected datasets of handwritten texts in Assamese and Telugu languages due to their unavailability in the desired form. Our approach achieved an average segmentation accuracy of 93.61%, 85.96%, and 88.74% for lines, words, and characters for both languages. The key motivation behind opting for a top-down approach is two-fold: firstly, it enhances the accuracy of character recognition, and secondly, it holds the potential for future use in language/script identification through the utilization of segmented lines and words. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Object detection

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Enhancing Academic Integrity in Online Assessments: Introducing an Effective Online Exam Proctoring Model using YOLO 2

Enhancing Academic Integrity in Online Assessments: Introduc...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Singh, Tripty Nair, Rekha R. Babu, Tina Duraisamy, Prakash Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Science and Engineering University of Wisconsin Green BayWI United States

The issue of cheating during exams has become a significant concern, gaining widespread attention from the public and media. This issue has been made worse by the transition of educational institutions to an online format, making it simpler to engage in academic dishonesty. To overcome this problem, developed an innovative solution known as the Online Exam Proctoring Model using You Only Look Once (YOLO) Model. With the use of cutting-edge computer vision algorithms, the proposed model makes use of data from cameras and microphones to automatically spot and prevent cheating during online exams. Face orientation, face spoofing detection, mouth tracking, audio analysis, mobile device recognition, and person counting are among the primary elements built into the model. Object detection algorithms, in conjunction with pre-trained YOLO weights, empower the model to detect mobile devices and count the number of individuals present in the exam environment. The automatic termination of exams upon detection of mobile devices, multiple persons, or no persons for 10 seconds acts as a strong deterrent against cheating. The suggested methodology created a reliable and effective tool for online examinations, guaranteeing the academic integrity and enabling impartial assessment of the skills and knowledge of learners. By implementing this solution, educational institutions can foster a secure online exam environment, promoting a culture of honesty and trust between students and educators. As navigate the evolving environment of education, the model stands as a reliable ally in upholding the credibility and authenticity of online learning experiences. © 2024 Elsevier B.V.. All rights reserved.

关键词： Object detection

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Real Time Traffic Light Timing Optimisation Using Reinforcement Learning

Real Time Traffic Light Timing Optimisation Using Reinforcem...

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2025 IEEE International Students' Conference on electrical, Electronics and computer science, SCEECS 2025

作者： Clement Andrew, A. Shri Vaisalini, L.C.K. Kavitha Devi, M.K. Thigarajar College of Engineering Department of Computer Science and Engineering Madurai625015 India

ISBN: (纸本)9798331529833

A major problem especially in urban areas is traffic congestion which causes increased travel times, pollution, and compromises safety. This paper presents the design of the Smart Traffic light timing Management System, optimized in real-time traffic light timings using reinforcement learning techniques. This system scans data from different sources, including vehicle count in a network and waiting time of the vehicle at a traffic stop, which are utilized to continuously to alter traffic signals based on current situations. Utilizing reinforcement learning algorithms, the system continuously learns and adapts to changing traffic scenarios, striving for minimal congestion. The simulations run in SUMO (Simulation of Urban Mobility) indicate a noticeable reduction in wait times and improved flow across roads. The results form the basis for the inclusion of advanced data analytics in traffic management: it represents an effective method of mitigating urban congestion problems. The study concludes that by leveraging real-time data and machine learning, the traditional traffic systems can be transformed into an intelligent responsive network, capable of enhancing the efficiency and safety of urban transportation. © 2025 IEEE.

关键词： Traffic congestion

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Enhancing the Stability of Organic Photovoltaics with Multifunctional Composite Coatings 52

Enhancing the Stability of Organic Photovoltaics with Multif...

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52nd IEEE Photovoltaic Specialist Conference, PVSC 2024

作者： Seralin, Aidar Zinedenova, Aruzhan Ibragim, Mukhammadali Aidarkhanov, Damir Ng, Annie Nazarbayev University Department of Electrical and Computer Engineering Astana010000 Kazakhstan

ISBN: (纸本)9781665464260

Organic photovoltaics (OPVs) show promise as a sustainable energy solution but lack long-term stability under real-world operating conditions. Exposure to UV light and moisture can cause performance degradation over time. Protective coatings that isolate devices from these factors without compromising OPV advantages like flexibility and light weight are helpful to enhance the device lifetime. This work develops down-converting fluorophore-embedded composite coatings to address the device stability. The coatings are designed to convert harmful UV photons into visible light that can be harvested by OPVs. The coating formulation was optimized to maximize downconversion while preventing fluorescence quenching. When the developed coatings applied to PM6:Y6 solar cells, the operational lifetime of devices are significantly extended. The tested device can retain 95% of its initial efficiency more than 600 hours under the maximum power point condition. © 2024 IEEE.

关键词： Fluorescence quenching

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Food ingredient recognition model via image and textual feature extraction and hybrid classification strategy

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International Journal of Reasoning-based Intelligent Systems 2024年第1期16卷 74-90页

作者： Madival, Sharanabasappa A. Jawaligi, Shivkumar S. Department of Computer Science and Engineering Faculty of Engineering and Technology Sharnbasva University India Department of Electronic and Communication Engineering Faculty of Engineering and Technology Sharnbasva University Karanataka Kalburgi India

This research work focuses on food recognition, especially, the identification of the ingredients from food images. Here, the developed model includes two stages namely: 1) feature extraction;2) classification. Initially, the image features and textual features will be extracted, where image features like SIFT and improved CNN-based deep features, textural features are extracted. Then, the hybrid classifier is used for the identification of food ingredients that combines the models like neural network (NN) as well as long short-term memory (LSTM). In order to make the accurate results, the weights of NN and LSTM are fine-tuned via the Chebyshev map evaluated teamwork optimisation (CME-TWO) algorithm. At the final stage, the primacy of the offered scheme is proven concerning varied metrics. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Feature extraction

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Dual-Channel MEMS Actuated Tunable Optical Filter Based on Polarization Insensitive Compound Ring Resonator for DWDM Networks 11th

Dual-Channel MEMS Actuated Tunable Optical Filter Based on P...

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11th EAI International Conference on Advancement of science and Technology, ICAST 2023

作者： Ayalew, Hailu Dessalegn Talabattula, Srinivas Annamalai, Pushparaghavan Electrical and Computer Engineering Faculty Bahir Dar University-Institute of Technology Bahir Dar Ethiopia Department of Electrical Communication Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)9783031641503

One of the key components of wavelength division multiplexing (WDM) networks is tunable optical filters (TOFs). This paper focused on the theoretical analysis and design of a new dual-channel micro-electromechanical system (MEMS) actuated tunable optical filter based on polarization insensitive compound ring resonators (PICRRs) for dense wavelength division multiplexing (DWDM) optical networking applications. It is basically structured on an optimally designed capacitive type microcantilever beam, which is fixed at both ends, with built-in PICRRs, which are placed at both fixed ends of the beam to gain linearly varying longitudinal strain and stress on the resonators. Also, the dimensions of the rings are properly chosen to achieve a full width at half maximum (FWHM) of 0.4078 nm with a flat pass band at 1 dB of 0.3626 nm and a good shape factor for both channels. It also shows a free spectral range of about 20.05 nm for the first channel and 20.046 nm for the second channel, with a high extinction ratio that allows filtering of adjacent channels of 50 GHz DWDM systems. Thus, when the electrostatic attraction force (EAF) is applied, the beam will bend, which induces strain and stress in the rings, which brings a change in effective refractive index of the rings and also affects the perimeter and coupling coefficients of the rings that leads to a change in the output spectrum shift, providing the tunability as high as 4.779 nm/μN, and it is capable of tuning up to 355 GHz with negligible variations of FWHM and channel spacing within the tunability range of the two channels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dense wavelength division multiplexing

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